Which of the following best describes the purpose of data wrangling?

Data wrangling, also known as data munging, involves transforming and cleaning raw data into a structured format suitable for analysis. This process includes handling missing values, correcting inconsistencies, standardizing formats, and often requires domain knowledge to ensure accuracy and relevance. Effective data wrangling makes analysis more efficient and reliable by ensuring the data is clean and usable. Option A is incorrect because storing raw data does not involve any transformation. Option B is incorrect as model building follows data wrangling. Option D is incorrect because creating dashboards is for data presentation, not wrangling. Option E is incorrect because KPIs are derived post-wrangling and analysis.